feature-kda

Setup R notebook Environment

knitr::opts_chunk$set(echo = TRUE)
Sys.setlocale("LC_CTYPE", "en_US.UTF-8")

[1] ""

Package load for Project

packages = c("sp","sf","tidyverse","tmap","rjson", "rgdal", "geojsonio", "GISTools",'spatialEco', "raster","dplyr", "spatstat",
             'maptools', 'leaflet') 
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p) 
  } 
  library(p,character.only = T) 
}

Importing Taiwan GDAM Subzone

Shapefile Object with Spatial data Import data for past 12 years

Requires translation of chinese character independetly (https://stat.ethz.ch/pipermail/r-sig-geo/2009-March/005323.html)

Taiwan follows EPSG 3826 (http://spatialreference.org/ref/epsg/twd97-tm2-zone-121/)

taiwan_ts_map_sp <- readOGR(dsn = "data/TAIWAN_TOWNSHIP", layer = "TOWN_MOI_1071226", stringsAsFactors=TRUE)

OGR data source with driver: ESRI Shapefile 
Source: "C:\Users\terence.tan.2015\Desktop\dangy\data\TAIWAN_TOWNSHIP", layer: "TOWN_MOI_1071226"
with 368 features
It has 7 fields

#taiwan <- readShapePoly("data/taiwan_data/")
taiwan <- readOGR(dsn = "data/taiwan_data", layer = "COUNTY_MOI_1071226", stringsAsFactors=TRUE)

OGR data source with driver: ESRI Shapefile 
Source: "C:\Users\terence.tan.2015\Desktop\dangy\data\taiwan_data", layer: "COUNTY_MOI_1071226"
with 22 features
It has 4 fields

#taiwan@proj4string<- CRS( "+init=epsg:3826 +proj=longlat +ellps=WGS84 +no_defs")
#taiwan.union <- aggregate(taiwan)
df_dengue <- jsonlite::fromJSON("data/dengue_case.json") %>% 
  filter(as.Date(Onset_day) >= "1999-01-01" & as.Date(Onset_day)< "2000-01-01")
# Check for NA values for coordinates
 sum(is.na(df_dengue$Minimum_statistical_area_center_point_X))

[1] 0

 sum(is.na(df_dengue$Minimum_statistical_area_center_point_Y))

[1] 0

 df_dengue<- df_dengue[!is.na(df_dengue$Minimum_statistical_area_center_point_X),]
 df_dengue<- df_dengue[!(df_dengue$Minimum_statistical_area_center_point_X == 'None'),]
 
 # Type conversion
 df_dengue[, c(10,11,19,23,24)] <- sapply(df_dengue[, c(10,11,19,23,24)], as.numeric)

NAs introduced by coercionNAs introduced by coercion

 
  # Transform into SF object
 sf_dengue <- st_as_sf(df_dengue, 
                       coords = c("Minimum_statistical_area_center_point_X",
                                  "Minimum_statistical_area_center_point_Y"),
                        crs = "+init=epsg:3826 +proj=longlat +ellps=WGS84 +no_defs",na.fail=FALSE)
sf_dengue <- na.omit(sf_dengue)
sp_dengue <- as(sf_dengue, 'Spatial')

sp_dengue <- as(sp_dengue, 'SpatialPoints')

tmap_mode("view")

tmap mode set to interactive viewing

tm_shape(taiwan)+
       tm_borders(col = "grey40",alpha=0.5)+
  # prevent zoom too much
  tm_view(alpha = 1, set.zoom.limits = c(7,21))+
  tm_layout(basemaps = c('OpenStreetMap'))+
  tm_shape(sp_dengue)+
  tm_dots(col ="red",size = 0.01) 

As of version 2.0, basemaps and basemaps.alpha have to be called from tm_view

tmap_mode("plot")

tmap mode set to plotting

Converting the generic sp format into spatstat’s ppp format

dengue_ppp <- as(sp_dengue, "ppp")
summary(dengue_ppp) 

Combining GP clinics points and the study area

dengue_ppp <- as(sp_dengue, "ppp")
summary(dengue_ppp) 

Planar point pattern:  699 points
Average intensity 148.3408 points per square unit

*Pattern contains duplicated points*

Coordinates are given to 6 decimal places

Window: rectangle = [120.15043, 121.79933] x [22.322371, 25.180104] units
Window area = 4.71212 square units

plot(dengue_ppp)

qt <- quadrat.test(dengueTW_ppp,                     
                   nx = 20, ny = 15)

plot(dengueTW_ppp) 
plot(qt, add = TRUE, cex =.1) 

tw_owin <- as(taiwan_ts_map_sp, "owin") 
plot(tw_owin) 

summary(tw_owin)

Window: polygonal boundary
1007 separate polygons (no holes)

enclosing rectangle: [118.13797, 124.56115] x [21.8956, 26.385278] units
Window area = 3.26423 square units
Fraction of frame area: 0.113

dengueTW_ppp = dengue_ppp[tw_owin]
dengueTW_ppp <- unique(dengueTW_ppp) 
summary(dengueTW_ppp)

Planar point pattern:  646 points
Average intensity 197.9024 points per square unit

Coordinates are given to 6 decimal places

Window: polygonal boundary
1007 separate polygons (no holes)

enclosing rectangle: [118.13797, 124.56115] x [21.8956, 26.385278] units
Window area = 3.26423 square units
Fraction of frame area: 0.113

#plot(dengueTW_ppp)

Kernel Density Estimation

#dengueTW_ppp.km <- rescale(dengueTW_ppp, 1000, "km") 
kde_taiwan_bw <- density(dengueTW_ppp.km, sigma=bw.diggle, edge=TRUE, kernel="gaussian")

#plot(kde_taiwan_bw)

plot(kde_taiwan_bw)